Instructions to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Adapters:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("undefined")
model.load_adapter("Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit", set_active=True)

Transformers

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit", dtype="auto")

llama-cpp-python

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
./llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Use Docker

docker model run hf.co/Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

LM Studio
Jan
Ollama
How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Ollama:
```
ollama run hf.co/Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
```

Unsloth Studio new

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit to start chatting

Docker Model Runner
How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Docker Model Runner:
```
docker model run hf.co/Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
```

Lemonade

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Run and chat with the model

lemonade run user.Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit-F16

List all available models

lemonade list

Uploaded model

Developed by: Agnuxo(https://github.com/Agnuxo1)
License: apache-2.0
Finetuned from model : Agnuxo/Mistral-NeMo-Minitron-8B-Base-Nebulal

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 162

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit

Unable to build the model tree, the base model loops to the model itself. Learn more.

Datasets used to train Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit

Collection including Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit

EnigmAgent + OpenCLAW + P2PCLAW ecosystem

Collection

Local-first AI tooling: encrypted MCP vault, eval tribunal, decentralized research network, neuromorphic GPU primitives. • 16 items • Updated 8 days ago • 1